Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
// Copyright 2019, OpenTelemetry Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package opentelemetry.proto.metrics.v1;
import "opentelemetry/proto/common/v1/common.proto";
import "opentelemetry/proto/resource/v1/resource.proto";
option csharp_namespace = "OpenTelemetry.Proto.Metrics.V1";
option java_multiple_files = true;
option java_package = "io.opentelemetry.proto.metrics.v1";
option java_outer_classname = "MetricsProto";
option go_package = "go.opentelemetry.io/proto/otlp/metrics/v1";
// MetricsData represents the metrics data that can be stored in a persistent
// storage, OR can be embedded by other protocols that transfer OTLP metrics
// data but do not implement the OTLP protocol.
//
// The main difference between this message and collector protocol is that
// in this message there will not be any "control" or "metadata" specific to
// OTLP protocol.
//
// When new fields are added into this message, the OTLP request MUST be updated
// as well.
message MetricsData {
// An array of ResourceMetrics.
// For data coming from a single resource this array will typically contain
// one element. Intermediary nodes that receive data from multiple origins
// typically batch the data before forwarding further and in that case this
// array will contain multiple elements.
repeated ResourceMetrics resource_metrics = 1;
}
// A collection of ScopeMetrics from a Resource.
message ResourceMetrics {
reserved 1000;
// The resource for the metrics in this message.
// If this field is not set then no resource info is known.
opentelemetry.proto.resource.v1.Resource resource = 1;
// A list of metrics that originate from a resource.
repeated ScopeMetrics scope_metrics = 2;
// This schema_url applies to the data in the "resource" field. It does not apply
// to the data in the "scope_metrics" field which have their own schema_url field.
string schema_url = 3;
}
// A collection of Metrics produced by an Scope.
message ScopeMetrics {
// The instrumentation scope information for the metrics in this message.
// Semantically when InstrumentationScope isn't set, it is equivalent with
// an empty instrumentation scope name (unknown).
opentelemetry.proto.common.v1.InstrumentationScope scope = 1;
// A list of metrics that originate from an instrumentation library.
repeated Metric metrics = 2;
// This schema_url applies to all metrics in the "metrics" field.
string schema_url = 3;
}
// Defines a Metric which has one or more timeseries. The following is a
// brief summary of the Metric data model. For more details, see:
//
// https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/metrics/data-model.md
//
//
// The data model and relation between entities is shown in the
// diagram below. Here, "DataPoint" is the term used to refer to any
// one of the specific data point value types, and "points" is the term used
// to refer to any one of the lists of points contained in the Metric.
//
// - Metric is composed of a metadata and data.
// - Metadata part contains a name, description, unit.
// - Data is one of the possible types (Sum, Gauge, Histogram, Summary).
// - DataPoint contains timestamps, attributes, and one of the possible value type
// fields.
//
// Metric
// +------------+
// |name |
// |description |
// |unit | +------------------------------------+
// |data |---> |Gauge, Sum, Histogram, Summary, ... |
// +------------+ +------------------------------------+
//
// Data [One of Gauge, Sum, Histogram, Summary, ...]
// +-----------+
// |... | // Metadata about the Data.
// |points |--+
// +-----------+ |
// | +---------------------------+
// | |DataPoint 1 |
// v |+------+------+ +------+ |
// +-----+ ||label |label |...|label | |
// | 1 |-->||value1|value2|...|valueN| |
// +-----+ |+------+------+ +------+ |
// | . | |+-----+ |
// | . | ||value| |
// | . | |+-----+ |
// | . | +---------------------------+
// | . | .
// | . | .
// | . | .
// | . | +---------------------------+
// | . | |DataPoint M |
// +-----+ |+------+------+ +------+ |
// | M |-->||label |label |...|label | |
// +-----+ ||value1|value2|...|valueN| |
// |+------+------+ +------+ |
// |+-----+ |
// ||value| |
// |+-----+ |
// +---------------------------+
//
// Each distinct type of DataPoint represents the output of a specific
// aggregation function, the result of applying the DataPoint's
// associated function of to one or more measurements.
//
// All DataPoint types have three common fields:
// - Attributes includes key-value pairs associated with the data point
// - TimeUnixNano is required, set to the end time of the aggregation
// - StartTimeUnixNano is optional, but strongly encouraged for DataPoints
// having an AggregationTemporality field, as discussed below.
//
// Both TimeUnixNano and StartTimeUnixNano values are expressed as
// UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January 1970.
//
// # TimeUnixNano
//
// This field is required, having consistent interpretation across
// DataPoint types. TimeUnixNano is the moment corresponding to when
// the data point's aggregate value was captured.
//
// Data points with the 0 value for TimeUnixNano SHOULD be rejected
// by consumers.
//
// # StartTimeUnixNano
//
// StartTimeUnixNano in general allows detecting when a sequence of
// observations is unbroken. This field indicates to consumers the
// start time for points with cumulative and delta
// AggregationTemporality, and it should be included whenever possible
// to support correct rate calculation. Although it may be omitted
// when the start time is truly unknown, setting StartTimeUnixNano is
// strongly encouraged.
message Metric {
reserved 4, 6, 8;
// name of the metric, including its DNS name prefix. It must be unique.
string name = 1;
// description of the metric, which can be used in documentation.
string description = 2;
// unit in which the metric value is reported. Follows the format
// described by http://unitsofmeasure.org/ucum.html.
string unit = 3;
// Data determines the aggregation type (if any) of the metric, what is the
// reported value type for the data points, as well as the relatationship to
// the time interval over which they are reported.
oneof data {
Gauge gauge = 5;
Sum sum = 7;
Histogram histogram = 9;
ExponentialHistogram exponential_histogram = 10;
Summary summary = 11;
}
}
// Gauge represents the type of a scalar metric that always exports the
// "current value" for every data point. It should be used for an "unknown"
// aggregation.
//
// A Gauge does not support different aggregation temporalities. Given the
// aggregation is unknown, points cannot be combined using the same
// aggregation, regardless of aggregation temporalities. Therefore,
// AggregationTemporality is not included. Consequently, this also means
// "StartTimeUnixNano" is ignored for all data points.
message Gauge {
repeated NumberDataPoint data_points = 1;
}
// Sum represents the type of a scalar metric that is calculated as a sum of all
// reported measurements over a time interval.
message Sum {
repeated NumberDataPoint data_points = 1;
// aggregation_temporality describes if the aggregator reports delta changes
// since last report time, or cumulative changes since a fixed start time.
AggregationTemporality aggregation_temporality = 2;
// If "true" means that the sum is monotonic.
bool is_monotonic = 3;
}
// Histogram represents the type of a metric that is calculated by aggregating
// as a Histogram of all reported measurements over a time interval.
message Histogram {
repeated HistogramDataPoint data_points = 1;
// aggregation_temporality describes if the aggregator reports delta changes
// since last report time, or cumulative changes since a fixed start time.
AggregationTemporality aggregation_temporality = 2;
}
// ExponentialHistogram represents the type of a metric that is calculated by aggregating
// as a ExponentialHistogram of all reported double measurements over a time interval.
message ExponentialHistogram {
repeated ExponentialHistogramDataPoint data_points = 1;
// aggregation_temporality describes if the aggregator reports delta changes
// since last report time, or cumulative changes since a fixed start time.
AggregationTemporality aggregation_temporality = 2;
}
// Summary metric data are used to convey quantile summaries,
// a Prometheus (see: https://prometheus.io/docs/concepts/metric_types/#summary)
// and OpenMetrics (see: https://github.com/OpenObservability/OpenMetrics/blob/4dbf6075567ab43296eed941037c12951faafb92/protos/prometheus.proto#L45)
// data type. These data points cannot always be merged in a meaningful way.
// While they can be useful in some applications, histogram data points are
// recommended for new applications.
message Summary {
repeated SummaryDataPoint data_points = 1;
}
// AggregationTemporality defines how a metric aggregator reports aggregated
// values. It describes how those values relate to the time interval over
// which they are aggregated.
enum AggregationTemporality {
// UNSPECIFIED is the default AggregationTemporality, it MUST not be used.
AGGREGATION_TEMPORALITY_UNSPECIFIED = 0;
// DELTA is an AggregationTemporality for a metric aggregator which reports
// changes since last report time. Successive metrics contain aggregation of
// values from continuous and non-overlapping intervals.
//
// The values for a DELTA metric are based only on the time interval
// associated with one measurement cycle. There is no dependency on
// previous measurements like is the case for CUMULATIVE metrics.
//
// For example, consider a system measuring the number of requests that
// it receives and reports the sum of these requests every second as a
// DELTA metric:
//
// 1. The system starts receiving at time=t_0.
// 2. A request is received, the system measures 1 request.
// 3. A request is received, the system measures 1 request.
// 4. A request is received, the system measures 1 request.
// 5. The 1 second collection cycle ends. A metric is exported for the
// number of requests received over the interval of time t_0 to
// t_0+1 with a value of 3.
// 6. A request is received, the system measures 1 request.
// 7. A request is received, the system measures 1 request.
// 8. The 1 second collection cycle ends. A metric is exported for the
// number of requests received over the interval of time t_0+1 to
// t_0+2 with a value of 2.
AGGREGATION_TEMPORALITY_DELTA = 1;
// CUMULATIVE is an AggregationTemporality for a metric aggregator which
// reports changes since a fixed start time. This means that current values
// of a CUMULATIVE metric depend on all previous measurements since the
// start time. Because of this, the sender is required to retain this state
// in some form. If this state is lost or invalidated, the CUMULATIVE metric
// values MUST be reset and a new fixed start time following the last
// reported measurement time sent MUST be used.
//
// For example, consider a system measuring the number of requests that
// it receives and reports the sum of these requests every second as a
// CUMULATIVE metric:
//
// 1. The system starts receiving at time=t_0.
// 2. A request is received, the system measures 1 request.
// 3. A request is received, the system measures 1 request.
// 4. A request is received, the system measures 1 request.
// 5. The 1 second collection cycle ends. A metric is exported for the
// number of requests received over the interval of time t_0 to
// t_0+1 with a value of 3.
// 6. A request is received, the system measures 1 request.
// 7. A request is received, the system measures 1 request.
// 8. The 1 second collection cycle ends. A metric is exported for the
// number of requests received over the interval of time t_0 to
// t_0+2 with a value of 5.
// 9. The system experiences a fault and loses state.
// 10. The system recovers and resumes receiving at time=t_1.
// 11. A request is received, the system measures 1 request.
// 12. The 1 second collection cycle ends. A metric is exported for the
// number of requests received over the interval of time t_1 to
// t_0+1 with a value of 1.
//
// Note: Even though, when reporting changes since last report time, using
// CUMULATIVE is valid, it is not recommended. This may cause problems for
// systems that do not use start_time to determine when the aggregation
// value was reset (e.g. Prometheus).
AGGREGATION_TEMPORALITY_CUMULATIVE = 2;
}
// DataPointFlags is defined as a protobuf 'uint32' type and is to be used as a
// bit-field representing 32 distinct boolean flags. Each flag defined in this
// enum is a bit-mask. To test the presence of a single flag in the flags of
// a data point, for example, use an expression like:
//
// (point.flags & DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK) == DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK
//
enum DataPointFlags {
// The zero value for the enum. Should not be used for comparisons.
// Instead use bitwise "and" with the appropriate mask as shown above.
DATA_POINT_FLAGS_DO_NOT_USE = 0;
// This DataPoint is valid but has no recorded value. This value
// SHOULD be used to reflect explicitly missing data in a series, as
// for an equivalent to the Prometheus "staleness marker".
DATA_POINT_FLAGS_NO_RECORDED_VALUE_MASK = 1;
// Bits 2-31 are reserved for future use.
}
// NumberDataPoint is a single data point in a timeseries that describes the
// time-varying scalar value of a metric.
message NumberDataPoint {
reserved 1;
// The set of key/value pairs that uniquely identify the timeseries from
// where this point belongs. The list may be empty (may contain 0 elements).
// Attribute keys MUST be unique (it is not allowed to have more than one
// attribute with the same key).
repeated opentelemetry.proto.common.v1.KeyValue attributes = 7;
// StartTimeUnixNano is optional but strongly encouraged, see the
// the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 start_time_unix_nano = 2;
// TimeUnixNano is required, see the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 time_unix_nano = 3;
// The value itself. A point is considered invalid when one of the recognized
// value fields is not present inside this oneof.
oneof value {
double as_double = 4;
sfixed64 as_int = 6;
}
// (Optional) List of exemplars collected from
// measurements that were used to form the data point
repeated Exemplar exemplars = 5;
// Flags that apply to this specific data point. See DataPointFlags
// for the available flags and their meaning.
uint32 flags = 8;
}
// HistogramDataPoint is a single data point in a timeseries that describes the
// time-varying values of a Histogram. A Histogram contains summary statistics
// for a population of values, it may optionally contain the distribution of
// those values across a set of buckets.
//
// If the histogram contains the distribution of values, then both
// "explicit_bounds" and "bucket counts" fields must be defined.
// If the histogram does not contain the distribution of values, then both
// "explicit_bounds" and "bucket_counts" must be omitted and only "count" and
// "sum" are known.
message HistogramDataPoint {
reserved 1;
// The set of key/value pairs that uniquely identify the timeseries from
// where this point belongs. The list may be empty (may contain 0 elements).
// Attribute keys MUST be unique (it is not allowed to have more than one
// attribute with the same key).
repeated opentelemetry.proto.common.v1.KeyValue attributes = 9;
// StartTimeUnixNano is optional but strongly encouraged, see the
// the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 start_time_unix_nano = 2;
// TimeUnixNano is required, see the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 time_unix_nano = 3;
// count is the number of values in the population. Must be non-negative. This
// value must be equal to the sum of the "count" fields in buckets if a
// histogram is provided.
fixed64 count = 4;
// sum of the values in the population. If count is zero then this field
// must be zero.
//
// Note: Sum should only be filled out when measuring non-negative discrete
// events, and is assumed to be monotonic over the values of these events.
// Negative events *can* be recorded, but sum should not be filled out when
// doing so. This is specifically to enforce compatibility w/ OpenMetrics,
// see: https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#histogram
optional double sum = 5;
// bucket_counts is an optional field contains the count values of histogram
// for each bucket.
//
// The sum of the bucket_counts must equal the value in the count field.
//
// The number of elements in bucket_counts array must be by one greater than
// the number of elements in explicit_bounds array.
repeated fixed64 bucket_counts = 6;
// explicit_bounds specifies buckets with explicitly defined bounds for values.
//
// The boundaries for bucket at index i are:
//
// (-infinity, explicit_bounds[i]] for i == 0
// (explicit_bounds[i-1], explicit_bounds[i]] for 0 < i < size(explicit_bounds)
// (explicit_bounds[i-1], +infinity) for i == size(explicit_bounds)
//
// The values in the explicit_bounds array must be strictly increasing.
//
// Histogram buckets are inclusive of their upper boundary, except the last
// bucket where the boundary is at infinity. This format is intentionally
// compatible with the OpenMetrics histogram definition.
repeated double explicit_bounds = 7;
// (Optional) List of exemplars collected from
// measurements that were used to form the data point
repeated Exemplar exemplars = 8;
// Flags that apply to this specific data point. See DataPointFlags
// for the available flags and their meaning.
uint32 flags = 10;
// min is the minimum value over (start_time, end_time].
optional double min = 11;
// max is the maximum value over (start_time, end_time].
optional double max = 12;
}
// ExponentialHistogramDataPoint is a single data point in a timeseries that describes the
// time-varying values of a ExponentialHistogram of double values. A ExponentialHistogram contains
// summary statistics for a population of values, it may optionally contain the
// distribution of those values across a set of buckets.
//
message ExponentialHistogramDataPoint {
// The set of key/value pairs that uniquely identify the timeseries from
// where this point belongs. The list may be empty (may contain 0 elements).
// Attribute keys MUST be unique (it is not allowed to have more than one
// attribute with the same key).
repeated opentelemetry.proto.common.v1.KeyValue attributes = 1;
// StartTimeUnixNano is optional but strongly encouraged, see the
// the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 start_time_unix_nano = 2;
// TimeUnixNano is required, see the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 time_unix_nano = 3;
// count is the number of values in the population. Must be
// non-negative. This value must be equal to the sum of the "bucket_counts"
// values in the positive and negative Buckets plus the "zero_count" field.
fixed64 count = 4;
// sum of the values in the population. If count is zero then this field
// must be zero.
//
// Note: Sum should only be filled out when measuring non-negative discrete
// events, and is assumed to be monotonic over the values of these events.
// Negative events *can* be recorded, but sum should not be filled out when
// doing so. This is specifically to enforce compatibility w/ OpenMetrics,
// see: https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#histogram
optional double sum = 5;
// scale describes the resolution of the histogram. Boundaries are
// located at powers of the base, where:
//
// base = (2^(2^-scale))
//
// The histogram bucket identified by `index`, a signed integer,
// contains values that are greater than (base^index) and
// less than or equal to (base^(index+1)).
//
// The positive and negative ranges of the histogram are expressed
// separately. Negative values are mapped by their absolute value
// into the negative range using the same scale as the positive range.
//
// scale is not restricted by the protocol, as the permissible
// values depend on the range of the data.
sint32 scale = 6;
// zero_count is the count of values that are either exactly zero or
// within the region considered zero by the instrumentation at the
// tolerated degree of precision. This bucket stores values that
// cannot be expressed using the standard exponential formula as
// well as values that have been rounded to zero.
//
// Implementations MAY consider the zero bucket to have probability
// mass equal to (zero_count / count).
fixed64 zero_count = 7;
// positive carries the positive range of exponential bucket counts.
Buckets positive = 8;
// negative carries the negative range of exponential bucket counts.
Buckets negative = 9;
// Buckets are a set of bucket counts, encoded in a contiguous array
// of counts.
message Buckets {
// Offset is the bucket index of the first entry in the bucket_counts array.
//
// Note: This uses a varint encoding as a simple form of compression.
sint32 offset = 1;
// bucket_counts is an array of count values, where bucket_counts[i] carries
// the count of the bucket at index (offset+i). bucket_counts[i] is the count
// of values greater than base^(offset+i) and less than or equal to
// base^(offset+i+1).
//
// Note: By contrast, the explicit HistogramDataPoint uses
// fixed64. This field is expected to have many buckets,
// especially zeros, so uint64 has been selected to ensure
// varint encoding.
repeated uint64 bucket_counts = 2;
}
// Flags that apply to this specific data point. See DataPointFlags
// for the available flags and their meaning.
uint32 flags = 10;
// (Optional) List of exemplars collected from
// measurements that were used to form the data point
repeated Exemplar exemplars = 11;
// min is the minimum value over (start_time, end_time].
optional double min = 12;
// max is the maximum value over (start_time, end_time].
optional double max = 13;
// ZeroThreshold may be optionally set to convey the width of the zero
// region. Where the zero region is defined as the closed interval
// [-ZeroThreshold, ZeroThreshold].
// When ZeroThreshold is 0, zero count bucket stores values that cannot be
// expressed using the standard exponential formula as well as values that
// have been rounded to zero.
double zero_threshold = 14;
}
// SummaryDataPoint is a single data point in a timeseries that describes the
// time-varying values of a Summary metric.
message SummaryDataPoint {
reserved 1;
// The set of key/value pairs that uniquely identify the timeseries from
// where this point belongs. The list may be empty (may contain 0 elements).
// Attribute keys MUST be unique (it is not allowed to have more than one
// attribute with the same key).
repeated opentelemetry.proto.common.v1.KeyValue attributes = 7;
// StartTimeUnixNano is optional but strongly encouraged, see the
// the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 start_time_unix_nano = 2;
// TimeUnixNano is required, see the detailed comments above Metric.
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 time_unix_nano = 3;
// count is the number of values in the population. Must be non-negative.
fixed64 count = 4;
// sum of the values in the population. If count is zero then this field
// must be zero.
//
// Note: Sum should only be filled out when measuring non-negative discrete
// events, and is assumed to be monotonic over the values of these events.
// Negative events *can* be recorded, but sum should not be filled out when
// doing so. This is specifically to enforce compatibility w/ OpenMetrics,
// see: https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md#summary
double sum = 5;
// Represents the value at a given quantile of a distribution.
//
// To record Min and Max values following conventions are used:
// - The 1.0 quantile is equivalent to the maximum value observed.
// - The 0.0 quantile is equivalent to the minimum value observed.
//
// See the following issue for more context:
// https://github.com/open-telemetry/opentelemetry-proto/issues/125
message ValueAtQuantile {
// The quantile of a distribution. Must be in the interval
// [0.0, 1.0].
double quantile = 1;
// The value at the given quantile of a distribution.
//
// Quantile values must NOT be negative.
double value = 2;
}
// (Optional) list of values at different quantiles of the distribution calculated
// from the current snapshot. The quantiles must be strictly increasing.
repeated ValueAtQuantile quantile_values = 6;
// Flags that apply to this specific data point. See DataPointFlags
// for the available flags and their meaning.
uint32 flags = 8;
}
// A representation of an exemplar, which is a sample input measurement.
// Exemplars also hold information about the environment when the measurement
// was recorded, for example the span and trace ID of the active span when the
// exemplar was recorded.
message Exemplar {
reserved 1;
// The set of key/value pairs that were filtered out by the aggregator, but
// recorded alongside the original measurement. Only key/value pairs that were
// filtered out by the aggregator should be included
repeated opentelemetry.proto.common.v1.KeyValue filtered_attributes = 7;
// time_unix_nano is the exact time when this exemplar was recorded
//
// Value is UNIX Epoch time in nanoseconds since 00:00:00 UTC on 1 January
// 1970.
fixed64 time_unix_nano = 2;
// The value of the measurement that was recorded. An exemplar is
// considered invalid when one of the recognized value fields is not present
// inside this oneof.
oneof value {
double as_double = 3;
sfixed64 as_int = 6;
}
// (Optional) Span ID of the exemplar trace.
// span_id may be missing if the measurement is not recorded inside a trace
// or if the trace is not sampled.
bytes span_id = 4;
// (Optional) Trace ID of the exemplar trace.
// trace_id may be missing if the measurement is not recorded inside a trace
// or if the trace is not sampled.
bytes trace_id = 5;
}
